Don't have the exact prompt anymore but it was the warmup of the three.
Start by clarifying the problem and identifying the key operations needed (e.g., lookups, insertions, deletions). Then, choose an appropriate hashmap/dictionary structure and design the algorithm around it, explaining how it achieves the desired time and space complexity. Finally, walk through an example and discuss edge cases and potential optimizations.
Pro tip: At Meta, interviewers value clean, efficient code and clear communication. Always discuss trade-offs (e.g., time vs. space) and consider using a hashmap to optimize from O(n^2) to O(n) when possible.
Ask clarifying questions to fully grasp the requirements, constraints, and expected input/output. Identify if the problem involves counting, grouping, caching, or lookups.
Decide between hashmap, dictionary, set, or other structures based on needed operations (e.g., key-value pairs, frequency counts). Justify your choice.
Outline the steps using the chosen structure, focusing on how it improves efficiency. Consider using one-pass or two-pass approaches.
State the time and space complexity of your solution, and compare with alternative approaches (e.g., sorting, brute force).
Walk through examples, including edge cases (empty input, duplicates, large data). Discuss potential optimizations or follow-up questions.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
First clarify the problem: define what constitutes a 'Y' shape (e.g., three arms meeting at a center) and what operations are allowed (e.g., changing a cell's value). Then model it as an optimization problem: for each possible center and arm lengths, compute the cost to transform the grid into a Y, and take the minimum. Use dynamic programming or precomputation to efficiently calculate costs for all possible Y shapes.
Pro tip: Before diving into code, discuss edge cases and constraints with the interviewer—such as grid size, allowed operations, and whether the Y must be symmetric. This shows you think about problem definition and scalability, which is crucial at Meta.
Ask questions to define the 'Y' shape precisely: must it be symmetric? What are the allowed operations (e.g., flip a cell, change value)? What are the grid dimensions and value types?
Determine how to compute the cost to transform a given set of cells into the target values. For example, if operations are bit flips, cost is the number of mismatches.
Identify all possible Y shapes by iterating over possible centers and arm lengths. For each, compute the cost efficiently using precomputed prefix sums or dynamic programming.
Use techniques like precomputing row/column/diagonal sums to quickly calculate the cost for each candidate Y, reducing time complexity from O(N^3) to O(N^2) or better.
Track the minimum cost across all valid Y shapes and return it. Discuss time and space complexity and potential optimizations.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
First, clarify the problem and identify the monotonic property (increasing or decreasing) that the stack will maintain. Then, walk through a small example to illustrate how the stack helps find the next greater/smaller element or compute areas, and finally, code the solution with O(n) time and O(n) space complexity.
Pro tip: Explicitly state the invariant of the monotonic stack (e.g., 'stack contains indices of elements in increasing order of value') and how it guarantees each element is pushed and popped at most once, leading to linear time. This shows deep understanding and reassures the interviewer about efficiency.
Restate the problem in your own words and ask clarifying questions about input constraints, expected output, and edge cases.
Determine whether a monotonic increasing or decreasing stack is needed based on the problem (e.g., next greater element uses decreasing stack).
Trace the algorithm on a small example, showing how elements are pushed/popped and how the result is built.
Write clean code using a stack (often storing indices) and a loop, handling edge cases like empty input or duplicates.
Explain that each element is pushed and popped at most once, giving O(n) time and O(n) space, then test with edge cases.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.